app.py

import gradio as gr
import os
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint

api_token = os.getenv("HF_TOKEN")

# Available LLMs
list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

# Load and split PDF document
def load_doc(list_file_path):
    loaders = [PyPDFLoader(file_path) for file_path in list_file_path]
    pages = [page for loader in loaders for page in loader.load()]
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
    return text_splitter.split_documents(pages)

# Create vector database
def create_db(splits):
    embeddings = HuggingFaceEmbeddings()
    return FAISS.from_documents(splits, embeddings)

# Initialize LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        huggingfacehub_api_token=api_token,
        temperature=temperature,
        max_new_tokens=max_tokens,
        top_k=top_k,
    )
    
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key="answer",
        return_messages=True,
    )

    retriever = vector_db.as_retriever()
    return ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff",
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )

# Initialize database
def initialize_database(list_file_obj, progress=gr.Progress()):
    list_file_path = [file.name for file in list_file_obj if file is not None]
    doc_splits = load_doc(list_file_path)
    vector_db = create_db(doc_splits)
    return vector_db, "✅ Vector database created successfully!"

# Initialize LLM
def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    llm_name = list_llm[llm_option]
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
    return qa_chain, "✅ Chatbot initialized. Ready to assist!"

# Format chat history for better readability
def format_chat_history(message, chat_history):
    return [f"User: {user_message}\nAssistant: {bot_message}" for user_message, bot_message in chat_history]

# Handle conversation
def conversation(qa_chain, message, history):
    formatted_chat_history = format_chat_history(message, history)
    response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"].split("Helpful Answer:")[-1].strip() if "Helpful Answer:" in response["answer"] else response["answer"]
    response_sources = response["source_documents"]
    
    # Extract sources with their pages
    sources = [(src.page_content.strip(), src.metadata["page"] + 1) for src in response_sources[:3]]
    new_history = history + [(message, response_answer)]
    return qa_chain, gr.update(value=""), new_history, *(item for sublist in sources for item in sublist)

# File upload handling
def upload_file(file_obj):
    return [file.name for file in file_obj]

# Gradio UI
def demo():
    with gr.Blocks() as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        gr.HTML("""
        <div style="background-color: #101010; padding: 15px; border-radius: 0px;">
            <h1 style="text-align: center; color: white;">📄 DocuQuery AI</h1>
        </div>
        <div style="background-color: #101010; padding: 15px; border-radius: 0px; margin-bottom: 20px;">
            <p style="color: white; font-size: 16px; text-align: center; font-weight: normal;">
                This chatbot enables you to query your PDF documents using Retrieval-Augmented Generation (RAG).<br>  
                🛑 Please refrain from uploading confidential documents! <br>
                This is only for education purpose.
            </p>
        </div>
        """)
        
        with gr.Row():
            with gr.Column(scale=86):
                gr.Markdown("### Step 1: Upload PDF files and Initialize RAG Pipeline")
                document = gr.Files(height=300, file_count="multiple", file_types=[".pdf"], interactive=True, label="Upload PDF Files")
                db_btn = gr.Button("Create Vector Database")
                db_progress = gr.Textbox(value="⏳ Waiting for input...", show_label=False)
                
                gr.Markdown("### Step 2: Configure Large Language Model (LLM)")
                llm_btn = gr.Radio(list_llm_simple, label="Select LLM", value=list_llm_simple[0], type="index")
                
                with gr.Accordion("LLM Settings (Optional)", open=False):
                    slider_temperature = gr.Slider(0.01, 1.0, 0.5, 0.1, label="Temperature")
                    slider_maxtokens = gr.Slider(128, 4096, 2048, 128, label="Max Tokens")
                    slider_topk = gr.Slider(1, 10, 3, 1, label="Top-k")
                qachain_btn = gr.Button("Initialize Chatbot")
                llm_progress = gr.Textbox(value="⏳ Waiting for LLM setup...", show_label=False)

            with gr.Column(scale=200):
                gr.Markdown("### Step 3: Chat with Your Document")
                chatbot = gr.Chatbot(height=505)
                
                with gr.Accordion("Context from Source Document", open=False):
                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                    source1_page = gr.Number(label="Page", scale=1)
                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
                    source2_page = gr.Number(label="Page", scale=1)
                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                    source3_page = gr.Number(label="Page", scale=1)
                
                msg = gr.Textbox(placeholder="Type your question here...", container=True)
                submit_btn = gr.Button("Submit")
                clear_btn = gr.ClearButton([msg, chatbot], value="Clear Chat")

        # Event bindings
        db_btn.click(initialize_database, [document], [vector_db, db_progress])
        qachain_btn.click(initialize_LLM, [llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], [qa_chain, llm_progress])
        msg.submit(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])
        submit_btn.click(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])
        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0], None, [chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])

    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()